Summary of Explainable Finite-memory Policies For Partially Observable Markov Decision Processes, by Muqsit Azeem et al.
Explainable Finite-Memory Policies for Partially Observable Markov Decision Processes
by Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav, Jan Kretinsky
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on improving the explainability of finite-memory policies in Partially Observable Markov Decision Processes (POMDPs). POMDPs are used for decision-making under uncertainty and partial observability, but their complexity makes it hard to implement optimal policies. To address this issue, the authors propose a representation of finite-memory policies using Mealy machines and decision trees. This combination yields smaller, more interpretable policies. The paper also provides a translation from standard FSC-form policies to the new representation, demonstrating its generalizability to other types of finite-memory policies. Additionally, the authors identify specific properties of “attractor-based” policies that allow for even simpler representations. Case studies illustrate the improved explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to make decisions when there’s not enough information. This paper helps us understand how to make these decisions by creating a new way to represent them. Right now, it’s hard to understand why certain decisions are being made because they’re too complicated. The authors combine two ideas to create simpler and more understandable policies. They show that this new method works for different types of decision-making problems. By making these policies easier to understand, we can make better decisions in situations where we don’t have all the information. |
Keywords
» Artificial intelligence » Translation
